prompts.txt

Excellent — let’s continue in this **user-centric, agent-aware, future-tuned vein**, and shape the next layer of `NeuroLake++` to be **not just a file format**, but a foundation for **cognitive tooling, RAG agents, and long-term adaptive memory**.  ---  ## 🧠 Let's Build the `NeuroLake++` Cognitive Spec – Layer by Layer  ---  ### 1. 📜 **Global Manifest File** (`manifest.json`)  Describes all contents in a "NeuroLake Object Group" (e.g., an S3 prefix or ZIP archive), defines modality coverage, versioning, and affordances.  ```json {   "neuro_lake_version": "0.4.1",   "description": "Memory & knowledge corpus for legal assistant GPT agent",   "modality_map": {     "text": true,     "image": true,     "pdf": true,     "audio": false,     "embedding": true   },   "index_types": ["dense", "sparse", "graph"],   "agent_hooks": ["summarize", "retrieve", "chain_reasoning"],   "chunks": [     "chunk_00001.json",     "chunk_00002.json",     ...   ] } ```  ---  ### 2. 🔹 **Chunk File** (`chunk_00001.json`)  Represents a unit of retrieval. Can be a memory, a doc fragment, a multimodal pair, etc.  ```json {   "chunk_id": "00001",   "type": "doc_fragment",   "modality": ["text", "embedding"],   "content": "The GDPR stipulates that personal data must be processed lawfully...",   "source": {     "origin": "GDPR_FullText_EN.pdf",     "section": "Article 5 - Principles"   },   "timestamp": "2023-08-22T11:45:12Z",   "tags": ["GDPR", "data processing", "privacy"],   "embedding": [0.14, 0.91, -0.55, ...],   "affordances": ["summarize", "cite", "retrieve"],   "version_info": {     "chunk_hash": "sha256:abcd123...",     "model_embedding": "OpenAI/text-embedding-3-large@2024-01",     "format": "utf8/text"   } } ```  ---  ### 3. 🧩 **Episodic Memory Block** (`chunk_00192.json`)  Useful for personal assistants, dev copilots, design bots...  ```json {   "chunk_id": "00192",   "type": "episodic_memory",   "agent": "gpt-legal-aide",   "user_context": {     "persona": "in-house legal advisor",     "session_intent": "understand data retention policies"   },   "summary": "User asked about retention periods for biometric data in smart meters.",   "reflection": "Follow up on DPA guidelines in Czech Republic.",   "embedding": [...],   "related_chunks": ["00001", "00017"],   "created": "2025-06-14T20:14:22Z" } ```  ---  ### 4. 🌐 **Graph Index File** (`graph_index.json`)  Describes entity-entity, chunk-chunk relationships across modalities.  ```json {   "nodes": [     {"id": "GDPR", "type": "legal_concept"},     {"id": "chunk_00001", "type": "text"},     {"id": "chunk_00192", "type": "memory"},     {"id": "UserLukas", "type": "user"}   ],   "edges": [     {"from": "chunk_00001", "to": "GDPR", "rel": "mentions"},     {"from": "chunk_00192", "to": "chunk_00001", "rel": "references"},     {"from": "UserLukas", "to": "chunk_00192", "rel": "authored"}   ] } ```  ---  ## 🔧 Planned Tooling  We can prototype this using:  * **DuckDB** to locally index & query JSONL files as a pseudo-RAG engine * **S3 folder layout** for deployable storage * **Local OpenAI-compatible embedding + mini-retriever (e.g., FAISS)** * Browser-side memory retrieval with **WASM + embedding hashing** (LlamaIndex in-browser)  ---  ## 🔥 What This Enables  | Capability                        | Why It Matters                                   | | --------------------------------- | ------------------------------------------------ | | 🎛️ Personalized, evolving memory | Agents that learn and remember across sessions   | | 🖼️ Multimodal memory-retrieval   | GPTs that *see, cite, and recall* across formats | | ⛓️ Graph-enhanced RAG             | Enables reasoning, tool-use, and disambiguation  | | 🧪 Time travel / version audit    | Trace how knowledge or memory evolved            | | 🔌 Plug-and-play RAG agents       | Universal context sources, future-ready APIs     |  ---  ## 🎯 Next: Where Do You Want to Take It?  Here are a few possible next steps:  1. **Local PoC**: DuckDB + `NeuroLake++`-style JSON index → basic RAG prototype. 2. **Memory Builder**: A tool to create episodic memory blocks from user sessions. 3. **S3 Storage Layout**: Full design of how `NeuroLake++` would live, grow, and update in an S3 bucket. 4. **Agent SDK**: Design the function signatures for agents to `remember()`, `reflect()`, and `query_memory()` from NeuroLake++.  Ready to go bold with the next step?
Give it a Matrix, Startrek like scifi look and add threejs effects
Awesome! Make it even more cooler looking!